Interactive full-body motion capture using infrared sensor network

Traditional motion capture (mocap) has been well-studied in visual science for a long time. More and more techniques are introduced each year to improve the quality of the mocap data. However up until a few years ago the field is mostly about capturing the precise animation to be used in different application after post processing such as studying biomechanics or rigging models in movies. These data sets are normally captured in complex laboratory environments with sophisticated equipment thus making motion capture a field that is mostly exclusive to professional animators. In addition, obtrusive sensors must be attached to actors and calibrated within the capturing system, resulting in limited and unnatural motion. In recent year the rise of computer vision and interactive entertainment opened the gate for a different type of motion capture which focuses on producing marker or mechanical sensorless motion capture. Furthermore a wide array of low-cost but with primitive and limited functions device are released that are easy to use for less mission critical applications. Beside the traditional problems of markerless systems such as data synchronization, and occlusion, these devices also have other limitation such as low resolution, excessive signal noise and narrow tracking range. In this thesis I will describe a new technique of using multiple infrared devices to process data from multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap. The method involves analyzing each individual sensor data, decompose and rebuild them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of captured signal from sensor. Each sensor operates on its own process and communicates through MPI. After each sensor provides the data to the main process, we synchronize data from all sensors into the same coordinate space. Finally we rebuild the final skeleton presentation by picking data with a combination of the most confident information. Our method emphasizes on the need of minimum calculation overhead for better real time performance while being able to maintain good scalability. These are specific contributions of this thesis: first, this technique offers a more accurate and precise mocap by making sure all the involved joints are properly tracked by at least one sensor at all time. Second, this method alleviates intrinsic shortfall of the device such as noise and occlusion. Third, it provides greater flexibility outside the geometric range limitation of one sensor which allows for greater movement freedom of an actor. And finally it does not require lengthy calibration and pre-processing procedures making this setup much more straightforward and easy to deploy in many application cases.